Reinforcement Learning-based Multi-channel Random Access for Massive Machine-Type Communication in 5G Networks

Reinforcement Learning-based Multi-channel Random Access for Massive Machine-Type Communication in 5G Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reinforcement Learning-based Multi-channel Random Access for Massive Machine-Type Communication in 5G Networks Ulugbek Amirsaidov, Kuanishbay Sadatdiynov, Bairam Turumbetov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6772782/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper takes into account the development trend of random access methods in wireless communication networks, traffic features and random access methods for massive Machine-Type Communications (mMTC). Existing multi-channel random access methods based on artificial intelligence mechanisms use neural networks due to a large number of system states. Many mMTC applications require a simple implementation of the random access method and low power consumption of mMTC devices. The paper proposes a simple multi-channel random access method based on reinforcement learning, called RL-MCSA. The states of the system (radio channel) are the total number of collisions during access. Only the previous state of the radio channel is required to make a decision. The reward for the decision is the total number of successful accesses. The decision is made in order to maximize the total number of successful packet transmissions in frequency-time channels (resource blocks) in the uplink direction of the 5G access network. Simulation-based experiment results show that the proposed method can improve the rate of successful access in each simulation cycle and the rate of utilization of the radio channel by 1.5-2 times more than existing, well-known algorithms. 5G access networks mMTC random radio channel access access probability access collision reinforcement learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6772782","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466119997,"identity":"cdd484c5-3376-49b9-beac-9520b5e66fa7","order_by":0,"name":"Ulugbek Amirsaidov","email":"","orcid":"","institution":"Tashkent University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Ulugbek","middleName":"","lastName":"Amirsaidov","suffix":""},{"id":466120002,"identity":"582cb82c-31eb-40e7-8256-1771fc5f1572","order_by":1,"name":"Kuanishbay Sadatdiynov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACCQSTsYGZoQJIMzM3kKLlDEgLI9FaQIrbIHrxapFs73324Mcfu3x+ieTGz4XzaqP524FaflRsw6lFmue4uWEPT7LlzBmJzdIztx3PnXGYsYGx58xtnFrkJNLYJHgkmA0MzhxsY+bddiy3AagF6EI8WuSfsUn+MaiHaplzLHc+IS3SEmxs0jwJhw0MjjcCtTTU5G4gpEWyJ41NWubAcQPJ9sZmaZ5jB3I3ArUcxOcXiePH2CTf/Kk24Gdmf/iZp6Yud975wwcf/KjArQUdHAaTB4hWDwR1pCgeBaNgFIyCEQIA2vhTrUkX9N8AAAAASUVORK5CYII=","orcid":"","institution":"Nukus State Technical University","correspondingAuthor":true,"prefix":"","firstName":"Kuanishbay","middleName":"","lastName":"Sadatdiynov","suffix":""},{"id":466120007,"identity":"092bbe61-3169-46aa-a47c-79815b000449","order_by":2,"name":"Bairam Turumbetov","email":"","orcid":"","institution":"Tashkent University of Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Bairam","middleName":"","lastName":"Turumbetov","suffix":""}],"badges":[],"createdAt":"2025-05-29 05:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6772782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6772782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84706329,"identity":"5f17afa2-7b5d-4299-9e3a-411572261521","added_by":"auto","created_at":"2025-06-16 12:24:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":676843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6772782/v1_covered_3937901a-cbc3-440f-b0c5-326b4f07aec1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reinforcement Learning-based Multi-channel Random Access for Massive Machine-Type Communication in 5G Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"5G access networks, mMTC, random radio channel access, access probability, access collision, reinforcement learning","lastPublishedDoi":"10.21203/rs.3.rs-6772782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6772782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper takes into account the development trend of random access methods in wireless communication networks, traffic features and random access methods for massive Machine-Type Communications (mMTC). 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